DRVY - China/Banking
DR VY
2023-04-29
1 Growth of US imports from China
2 Change in number of branches and deposits
Import sensitivity for each county was calculated by following
Autor,Dorn, and Hanson (2013). Data period 2000 to 2007.
Counties are categorized in to three groups: 1 - lowest third
sensitivity, 2 - middle third senstivitiy, 3 - top third sensitivity
County-level per capita number of branches and per capita deposits were normalized using the year 2000 values.
3 Branch openings and closings
Used SOD data to figure out if there is at least one new branch
opening in a given county-year (variable open) and at least
one branch closure (variable close).
Motivated by Per capita number of branches figure above, a dummy
variable post was defined as period after 2010
Ran the following regression \[ Y_{c,y} =
\beta \times post \times highimportsensitivity + CountyFE+YearFE
\]
highimportsensitivity is a dummy variable that takes
value 1 if the import sensitivity of the county is greater
than the median.
3.1 Regression
##
## ============================================================================
## Dependent variable:
## -----------------------------------------------------
## open log(1 + openings) close log(1 + closings)
## (1) (2) (3) (4)
## ----------------------------------------------------------------------------
## high_x
## (0.000) (0.000) (0.000) (0.000)
## post
## (0.000) (0.000) (0.000) (0.000)
## log(population) 0.106*** -0.626*** 0.044 -0.231***
## (0.038) (0.061) (0.035) (0.047)
## log(income_per_capita) 0.170*** 0.435*** 0.064** 0.258***
## (0.026) (0.034) (0.026) (0.029)
## unemp_rate -0.003 0.005** 0.0003 0.009***
## (0.002) (0.003) (0.002) (0.002)
## high_x:post -0.042*** -0.140*** 0.008 -0.075***
## (0.009) (0.014) (0.008) (0.012)
## ----------------------------------------------------------------------------
## Observations 49,662 49,662 49,662 49,662
## R2 0.438 0.749 0.422 0.748
## Adjusted R2 0.405 0.735 0.388 0.733
## ============================================================================
## Note: *p<0.1; **p<0.05; ***p<0.013.2 Figure
4 Deposit and Branch Change from 2008 to 2019
This is similar to main results of Autor,Dorn, and Hanson
(2013), but at county-level.
log(import sensitivity) is instrumented using
log(iv import sensitivity) which is based on the import
growth in other developed countries. (same as Autor,Dorn, and Hanson
(2013))
4.1 First Stage
log-log specification has more first stage power.
4.2 Descriptive statistics
##
## ============================================================================
## Statistic Mean St. Dev. Pctl(25) Median Pctl(75) N
## ----------------------------------------------------------------------------
## sod_br_change 88.679 16.795 79.000 89.476 99.000 2,691
## sod_br_pc_change 88.019 17.914 76.593 87.945 100.000 2,691
## sod_deposit_change 131.684 30.833 112.259 127.371 146.198 2,691
## sod_deposit_pc_change 129.942 27.972 113.094 127.168 143.267 2,691
## population 84,693.020 224,019.800 11,425.5 26,038 63,812.5 2,691
## income_per_capita 33,667.450 8,507.270 28,277 32,364 37,231.5 2,691
## import_sensitivity 14.490 4.775 12.757 13.565 14.949 2,691
## iv_import_sensitivity 4.646 3.372 3.169 3.924 5.153 2,691
## ----------------------------------------------------------------------------
4.3 Impact on county-level branches
The first table below regresses the change in number of branches at
county level from 2008 to 2019. Column 4 uses
per capita
branches change
##
## ======================================================================================================================================
## BR chg (%) BR chg (%) BR chg (%) BR percap chg (%) BR chg (%) BR chg (%) BR chg (%) BR chg (%)
## All Banks Small Banks Medium Banks Large Banks Contemporaneous
## (1) (2) (3) (4) (5) (6) (7) (8)
## --------------------------------------------------------------------------------------------------------------------------------------
## log(income_per_capita) 8.441*** 7.659*** 4.287** -1.316 5.952 -7.574 19.308 8.052***
## (2.893) (2.937) (2.022) (1.983) (5.846) (12.421) (13.125) (3.048)
## log(population) -2.916*** -2.770*** -1.375*** -3.809*** -3.147** 2.695 -3.327*** 3.138***
## (0.417) (0.441) (0.404) (0.396) (1.393) (2.144) (1.151) (0.802)
## `import_sensitivity(fit)` -0.098
## (0.115)
## `log(import_sensitivity)(fit)` -7.306* -5.526** -4.961** -2.497 -50.149*** 6.765 -12.231***
## (3.844) (2.314) (2.281) (6.328) (18.341) (7.973) (4.039)
## Constant 32.243 56.834* 70.917 296.627** -101.210 24.189
## (27.410) (31.428) (62.105) (135.580) (123.366) (32.498)
## --------------------------------------------------------------------------------------------------------------------------------------
## State FE Y Y
## Cond.F.Stat 15.38 45.33 52.47
## Observations 2,691 2,691 2,691 2,691 2,531 1,500 1,752 2,689
## R2 0.059 0.060 0.171 0.245 0.012 0.003 0.016 0.051
## Adjusted R2 0.058 0.059 0.157 0.232 0.011 0.001 0.014 0.050
## ======================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.014.4 Impact on county-level deposits
##
## ========================================================================================================================================
## BR chg (%) BR chg (%) BR chg (%) BR percap chg (%) BR chg (%) BR chg (%) BR chg (%) BR chg (%)
## All Banks Small Banks Medium Banks Large Banks Contemporaneous
## (1) (2) (3) (4) (5) (6) (7) (8)
## ----------------------------------------------------------------------------------------------------------------------------------------
## log(income_per_capita) 44.295*** 41.960*** 33.238*** 23.001*** -52.330 50.321 111.842* 23.418***
## (4.555) (4.594) (2.980) (2.806) (121.691) (41.413) (61.957) (7.500)
## log(population) 1.517** 1.967** 4.329*** 0.255 6.940 7.102 2.782 7.991***
## (0.739) (0.765) (0.489) (0.461) (5.846) (6.918) (5.499) (1.600)
## `import_sensitivity(fit)` -0.065
## (0.373)
## `log(import_sensitivity)(fit)` -19.864*** -7.237* -6.832* -67.877* -145.410*** -14.439 -32.678***
## (6.915) (4.366) (4.111) (39.537) (46.150) (20.450) (10.412)
## Constant -343.506*** -272.079*** 817.324 -2.772 -1,028.801* -101.820
## (45.297) (50.712) (1,307.638) (420.694) (601.441) (78.840)
## ----------------------------------------------------------------------------------------------------------------------------------------
## State FE Y Y
## Cond.F.Stat 15.38 45.33
## Observations 2,691 2,691 2,691 2,691 2,531 1,500 1,752 2,689
## R2 0.117 0.116 0.243 0.184 0.0004 0.006 0.064 0.067
## Adjusted R2 0.116 0.115 0.230 0.170 -0.001 0.004 0.062 0.066
## ========================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01